KR102091225B1 - Automated information retrieval - Google Patents

Abstract

A method, system and device for automated information retrieval, comprising a computer program encoded on a computer storage medium, comprising receiving relevance scores for a separate set of web resources, each relevance score being Indicates the relevance of the web resource corresponding to the search query; For each web resource in the set of web resources, obtaining a plurality of similarity scores, each similarity score for the web resource is determined by each deep link for a native application. Indicates the similarity between each referenced content and the web resource; For each of the deep links, based on each of the similarity scores between the content referenced by the deep link and the web resources and each of the relevance scores for the web resources, referenced by the deep link. Generating each quality score for the content to be made; Selecting deep links referencing the content, each quality score meeting a critical quality score; And in response to the search query, providing the selected deep links to a user device along with a plurality of web search results each referring to a corresponding web resource.

Description

Automated information retrieval

The Internet provides access to a variety of information. For example, digital image files, video and / or audio files, web pages resources R of a particular subject or a specific news article can be accessed over the Internet. Many of these resources in relation to web page resources are designed to facilitate the performance of certain functions such as banking, hotel reservations, shopping, etc., or to provide structured information such as online encyclopedias, movie databases, etc. do. Search engines crawl and index these resources to facilitate resource searches.

In addition, with the advent of tablet computers and smart phones, many native applications are currently provided that facilitate the performance of the same functions facilitated by the use of web page resources. In addition, native applications that do not have websites with similar content, such as games, are widely used in tablet computers and smartphones. Thus, search engines now facilitate searching for these native applications.

One of the processes by which search engines gather information about native applications is to access "deeplinks" for native applications. Deeplink is a command that designates a specific environment instance of a native application, and when selected on a user device, a native application can be configured to instantiate an environment instance of a specific native application. Native applications create environment instances for display within native applications on a user device. For example, a deep link may be a URI that designates a specific user interface to be instantiated when a native application is launched by using a specific native application, resource content to be accessed by the native application, and use of the deep link.

Now, the search engine also facilitates the search of these native applications. Thus, the user's information needs can be met by a search engine that provides search results that identify either (or both) the search results for a particular web page resource describing the native application and the native application itself. .

In general, this specification describes systems and methods for automated information retrieval. The system and method includes scoring content in native applications, so that even if native applications do not have corresponding web pages, one or more search results related to the native applications are provided in response to the query. To be included in Accordingly, the present system and method provides improved search and search.

In general, one of the innovative aspects of the invention described herein can be implemented in methods, the method comprising receiving relevance scores for a separate set of web resources, each relevance score being Indicates the relevance of the web resource corresponding to the search query; For each web resource in the set of web resources, obtaining a plurality of similarity scores, each similarity score for the web resource is determined by each deep link for a native application. Indicates the similarity between each referenced content and the web resource; For each of the deep links, based on each of the similarity scores between the content referenced by the deep link and the web resources and each of the relevance scores for the web resources, referenced by the deep link. Generating each quality score for the content to be made; Selecting deep links referencing the content, each quality score meeting a critical quality score; And in response to the search query, providing the selected deep links to a user device along with a plurality of web search results each referring to a corresponding web resource.

Implementations may include one or more of the following features. Before obtaining multiple similarity scores: for each web resource, generate the multiple similarity scores for the web resource from the content and the web resource. The step of generating a plurality of similarity scores is based on one or more of n-gram Jaccard similarity, a minimum hash, or a locality-sensitive hashing for the plurality of similarity scores. For each of the deep links, generating a respective quality score for the content referenced by the deep link includes, for each web resource, the content between the content referenced by the deep link and the web resource. Calculating a product of each of the similarity scores and each of the relevance scores for the web resource; And summing the respective products to produce respective quality scores. Each deep link for each native application designates a specific environment instance of the respective native application and, when selected on a user device, causes the respective native application to display each content referenced in the deep link. Lets you instantiate an instance of a native application. Each relevance score for each web resource is based on the rank of each web resource in the list of web resources ranked by the search engine. The step of selecting deep links referring to the content having each quality score satisfying the critical quality score is to select up to the maximum number of deep links referring to the content having a quality score meeting the critical quality score. Includes. Providing a plurality of deep links with a plurality of web search results to a user device includes providing a user ranked list of web search results and deep links to the user device. Providing the user device with an ordered list of web search results and deep links comprises, for each deep link, each relevance to the web search results to generate a normalized relevance score for the deep link. Normalizing each of the quality scores for the deep link with respect to scores; Ranking the web search results and deep links based on the relevance scores and the normalized relevance scores to generate a ranked list of web search results and deep links; And providing a ranking list of the web search results and deep links to a user device. Each content referenced by each deep link is not a web resource.

Certain embodiments of the subject matter of the invention described herein may be implemented to realize one or more of the following advantages. Some native applications do not have web pages corresponding to the native application. The system may rank these native applications without corresponding web and / or content pages, although they do not have relevance scores of existing corresponding web pages to use as a base metric.

Search results, including links to a particular location in a native application, with or without corresponding web pages (eg, mobile app), rank along with other search results (eg, search results for web pages). Can be ranked, so more relevant resources (apps or web pages) have a higher ranking. This inclusion of search results that link to locations within applications provides additional search result options, which may better meet the user's information needs.

The details of one or more embodiments of the subject matter of the invention described herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter of the present invention will become apparent from the detailed description, drawings and claims.

1 is a block diagram of an exemplary environment in which search and ranking of native applications occurs.
2A is a flow diagram of an example method for generating similarity scores between content and web resources in native applications.
2B is a flow diagram of an example method for scoring content in a native application.
3 is a diagram showing scoring content in a native application.
4 shows the native application search results provided with the web search results.
Like reference numerals and names in the various drawings indicate like elements.

The system scores content in native applications that do not have corresponding web pages. That is, the native application may display content that cannot be accessed from a web resource (eg, a web page) through a web browser. Content in a native application can be scored based on web resources similar to that content, which will be further described below.

In this specification, a native application creates environment instances for display on a user device within the native application's environment, and operates independently of the browser application on the user device. Native applications are applications specifically designed to run on specific user device operating systems and machine firmware. Thus, native applications differ from browser-based applications and browser-rendered resources. The latter requires that all or at least some elements or instructions be downloaded from the web server each time it is instantiated or rendered. In addition, browser-based applications and browser-rendered resources can be processed by all web-enabled mobile devices in the browser and are therefore not operating system specific.

When a search is triggered to include native application search results along with web search results, the native application index is searched for native applications and the native applications are scored. Various scoring signals can be used, including indexed content of the native application, user ratings of the native application, query popularity for queries received for searches of the application index, etc. . Native applications, once scored in response to a query, are ranked as follows, and one or more native application search results may be provided to the user device in response to the query.

Whether a native application search result is provided, and if so, the location of the native application search result relative to other search results is determined based on one or more filtering criteria and ranking criteria. Filtering criteria and ranking criteria may include ranking of a corresponding resource describing native applications for other resources, scores of native applications, and other factors.

These and other features are described in more detail below.

1 is a block diagram of an exemplary environment 100 in which search and ranking of native applications occurs. A computer network 102, such as the Internet, connects resource publisher websites 104, application publishers 106, user devices 108, and search engines 120.

The resource publisher website 104 includes one or more web resources 105 associated with a domain and is hosted by one or more servers in one or more locations. In general, a resource publisher website is a collection of web pages in Hypertext Markup Language (HTML) format that can include text, images, multimedia content, and programming elements. Each website 104 is maintained by a content publisher, which is the entity that controls, manages and / or owns the website 104.

A web page resource can be provided by the publisher website 104 over the network 102 and is any data having a resource address, for example a uniform resource locator (URL). Web resources can be HTML pages, image files, video files, audio files and feed sources, etc. Resources can include embedded information (eg, meta information and hyperlinks), and / or embedded commands (eg, client-side scripts). More generally, "resource" is anything that can be identified over the network and can also include native applications.

In addition, the application publisher website 106 may include one or more web resources 105 and may also provide a native application 107. As described above, the native application 107 is an application specifically designed to run on a particular user device operating system and machine firmware. Native application 107 may include multiple versions designed to operate on different platforms. For example, a native application corresponding to a movie database website may include a first native application running on a first type of smartphone, a second native application running on a second type of smartphone, and a first type of tablet. It may include a third native application running, and the like.

As used herein, "environment instance" is a display environment within a native application, and content such as text, images, etc. is displayed in the environment instance. The environment instance is specific to a particular native application, and the native application is specific to a particular operating system of the user device 108. Environment instances are different from rendered web resources, while environment instances are created within a native environment and are specific to a native environment, while web resources can be rendered in any browser compatible with web page resources and run on the user device. In terms of being system independent, the environment instance and the rendered web resource are different.

The user device 108 is an electronic device under the control of the user. Typically, user device 108 may request and receive web page resources 104 and native applications 107 via network 102. Exemplary user devices 108 include personal computers, mobile communication devices, and tablet computers.

To search web resources 105 and native application 107, search engine 120 accesses web index 116 and application index 114. Web index 116 is an index of web resources 105 established, for example, by crawling publisher websites 107. The application index 114 is an index of application pages for native applications 107, and is configured using the application data extractor, processor 110, and indexer 112. Although shown as separate indexes, the web index 116 and the application index 114 can be combined into one index.

User device 108 submits search queries to search engine 120. In response to each query, search engine 120 accesses web index 116 and optionally application index 114 to identify resources and applications that are relevant to the query, respectively. In general, a first type of search operation that implements the first search algorithm is used to retrieve the index 116, and a second type of search operation that implements the second other algorithm is used to index the application 114. Search for. The search engine 120 implements the resource scorer 132 process and the similarity scorer 136 process, where the resource scorer 132 generates relevance scores for web resources, and the similarity scorer 136 communicates with the web resources. Generate similarity scores between content in native applications. Content in native applications is not a web resource. The native application content scorer 134 process generates quality scores for content in native applications based on relevance and similarity scores. The native application content scorer 134 will be described in more detail below with reference to FIGS. 2 and 3. Various suitable search engine algorithms can be used to implement the resource scorer 132, similarity scorer 136, and native application content scorer 134.

The search engine 120 uses a search engine front end 138, such as a web server, to determine whether to search the native application index 114 and provide the native application search results to the user device. The search engine front end 138 sorts and presents search results to the user device 108 on which the query was received.

The web resource search result is data generated by the search engine 120, and identifies web resources and provides information satisfying a specific search query. The web resource search result for the resource may include a web page title, a snippet of text extracted from the resource, and a resource locator for the resource (eg, URL of the web page). The native application search results specify the native application and are generated in response to the search for the application index 114. The native application search results can include a "deep link" that specifies a specific environment instance of the native application, which can be configured (if selected) to instantiate the specified environment instance. For example, deep link may include a selection menu for a game environment; Or selection of a specific song for a music application; Or specific recipes for culinary applications; And so forth. For example, selection of a native application search result may cause a native application to be launched (if installed on the user device 108) and may create an environment instance referenced in the application search result in the form of a screenshot. Alternatively, the native application search results may include a “buy” (or “install”) command, which, if selected, allows the native application to be purchased (or free download) and installed on the user device. have.

The publisher 106 that provides the native application 107 can also provide the deep link 109 to the search engine 120. For example, the application publisher may provide a list of deep links 109 in the form of uniform resource identifiers (URIs) (or other types of commands specific to the native application published by the publisher). These deep links are deep links that the publisher 106 wants to crawl and index in the application index 114.

For many native applications 107, there are also web resources 111 describing native applications 107. An example of such a resource 111 is a product page in an online native application store. Product pages can be browsed using a web browser and indexed in web index 116. The web resource 111 may include a screen shot of a native application, a description of a user class, and the like. Typically, the web resource 111 is a web page specialized for the native application, and is used to facilitate the purchase and / or download of the native application.

In certain situations, according to the search query and corresponding web-based search results, the search engine 120 may include native application search results in a set of web page search results. The native application search results may be inserted, for example, in a location relative to the product web page search results for the native application, or alternatively, may entirely replace the product web page search results. This is further explained below with reference to FIG. 4.

2A is a flow diagram 200 of an example method of generating similarity scores between content and web resources in native applications. For convenience, the method 200 will be described in connection with a system having one or more computing devices running software to implement the method 200, for example, the search engine 120 of FIG. 1.

The system collects web resources (step 202). Web resources can be collected from a web index, such as web index 116 of FIG. 1.

The system acquires content in native applications (step 204). In some implementations, the content is content from the application page of the native application indexed within the application index, such as content from application index 114 of FIG. 1.

The system generates a similarity score between content and web resources (step 206). The system can generate similarity scores between a web resource and each content using any suitable method. For example, the system can generate a similarity score based on n-gram Jaccard similarity, minimum hash, or locality-sensitive hashing.

In some implementations, the system produces output of the form:

[wd_1, [(nac_1, s_11), (nac_2, s_12), (nac_3, s_13) ...],

wd_2, [(nac_1, s_21), (nac_2, s_22), (nac_3, s_23) ...], ...]

Here, wd_i (eg, wd_1 or wd_2) is the web document i, nac_j (eg, nac_1) is the native application content j, and s_ij (eg, s_11) is between the web document i and the native application content j. Similarity score. Also, s_ij = similarity (wd_i, nac_j) = similarity (nac_j, wd_i); similarity is a function that calculates the similarity score s_ij.

The system uses the output to generate quality scores for content in the native application, which will be described later with reference to FIG. 2A.

2B is a flow chart 208 of an example method for scoring content in native applications. For convenience, the method 208 will be described in connection with a system having one or more computing devices running software to implement the method 208, such as search engine 120 in FIG.

The system receives relevance scores for the set of web resources (step 210). Each web resource has a relevance score indicating the relevance between the web resource and the search query.

In some implementations, the relevance score is based on a ranking of web resources in a list of web resources ranked by a search engine. For example, the relevance score can be calculated using Equation 1 below:

(1)

(One)

Here, s is the number of search results in the list of search results in response to the search query, and r is the ranking of web resources in the list of search results.

For each web resource in the set of web resources, the system obtains a set of similarity scores for the web resource (step 212). The similarity score may be obtained from the output vector as described above with reference to FIG. 2A. Each similarity score for a web resource may indicate the similarity between each content and web resource in a native application without corresponding web or content pages.

Each content can be referenced by each deeplink to the native application. Each deep link specifies a specific environment instance of the native application, and when selected on the user device, causes the native application to instantiate an instance of each native application in which each content referenced in the deep link is displayed.

For each of the deep links, the system generates a respective quality score for the content referenced by the deep link (step 214). The quality score for the content referenced by the deeplink may be generated from similarity scores between content and web resources and relevance scores of the web resource. This will be described in more detail with reference to FIG. 3.

The system selects a deep link that references content with each quality score that meets the critical quality score (step 216). In some implementations, the system selects the maximum number of deep links with quality scores that satisfy the critical quality score. The maximum number can be determined by the system administrator.

The system provides the user device with selected deep links along with web search results each referring to a corresponding web resource (step 218). In response to the search query, the system can provide the selected deep links and web search results to the user device. In one embodiment, the system may provide the user device with a ranking list of web search results and deep links.

In some implementations of providing a ranked list, the system normalizes each quality score for the deep link, for each deep link, for each relevance score for web search results, normalization for the deep link Generated relevance scores. For example, if a particular relevance score can be a number within a range of predetermined numbers, the system can scale the quality score for the deep link to a proportional number within the range of numbers relative to the relevance score ( Using scaling factors).

After normalizing each quality score for each deep link, the system generates web search results and web links based on the relevance scores and the normalized relevance scores to generate an integrated ranking list of deep links. You can rank deep links. Next, the system can provide the user device with a ranked list of web search results and deep links, which will be further described below with reference to FIG. 4.

In some implementations, steps 210-218 are performed in response to a search query from the user. In some other implementations, generating similarity scores is performed as part of the backend process.

3 is an example 300 of scoring content in native applications using a native application content scorer of a search engine given a search query. The content may be an application page X that does not have corresponding web pages from a web index (eg, web index 116 in FIG. 1). The native application content scorer can generate a quality score X_quality 314 for the application page X.

To generate the quality score X_quality 314, the scorer can calculate the dot product between a vector of relevance scores for a set of web resources and a vector of similarity scores for a set of web resources. That is, the quality score can be calculated using Equation 2 below.

(2)

Here, x is an application page without a corresponding web page, resource _k is a k th web resource in a set of n web resources, a relevance function returns a relevance score, and a similarity function is between a k th web resource and an application page x Returns the similarity score indicating the similarity of.

As an example, A, B, and C can be web resources, such as web resources from web index 116 of FIG. 1. Thus, A_relevance 302 is the relevance score for A for the search query, AX_similarity 304 is the similarity score for how similar web resource A is for application page X, and B_relevance 306 is B for the search query. Is the relevance score for, BX_similarity (308) is a similarity score indicating how similar web resource B is to application X, C_relevance (310) is relevance score for C for the search query, and CX_similarity (312) is application X Is a similarity score indicating how similar Web resource C is, and so on. The scorer can calculate the product of A_relevance (302) and AX_similarity (304), the product of B_relevance (306) and BX_similarity (308), the product of C_relevance (310) and CX_similarity (312), and add the products X_quality score 314 may be generated.

The scorer can score the application page by calculating the dot product in this way for each application page in the application index that does not have corresponding web and / or content pages.

FIG. 4 is a diagram illustrating a search result of a native application provided with web search results. The search results may be provided in the browser application of the user device.

The browser application displays a view 401 of search results 404-410 provided by the search engine in response to the search query 402. One of the search results is a native application search result (ie, native application search result 408), and the other search results are web search results (ie, web search results 404, 406, 410). Search results 404-410 are shown in order of decreasing relevance score for web search results 404, 406, 410 native application search results 408.

The native application search result 408 is a deep link, and when the deep link is selected, the native application can cause an instance of each native application to be instantiated, and the content referred to in the native application search result 408 is a user. Displayed on the device.

Embodiments of the subject matter and functional operation of the present invention described herein may be implemented with digital electronic circuits, computer software or firmware, substantially implemented computer software or firmware, computer hardware, and the structures disclosed herein. And structural equivalents or combinations of one or more of these. Embodiments of the invention described herein are one or more computer programs for execution by a data processing apparatus, or one or more modules of computer program instructions encoded on a computer storage medium, for controlling operation of the data processing apparatus. It may be implemented, and the storage medium may be, for example, a tangible non-transitory computer storage medium. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of these. The computer program can include a number of files and / or can be arranged to run one or more data processing devices.

The term "data processing apparatus" includes all kinds of devices, devices and machines for processing data, and includes, for example, a programmable processor, computer, special purpose circuit, or multiprocessor or computer. In addition, the device may include, in addition to hardware, code that generates an execution environment for a computer program, such as processor firmware, a protocol stack, a database management system, operating system, or a combination thereof. You can.

The processes and logic flows described herein can be performed by one or more programmable computers or one or more special purpose logic circuits executing one or more computer programs to perform functions by manipulating input data and generating output. .

Although this specification includes a number of specific implementation details, they should not be construed as limitations on any invention or claims, but rather on the description of features that may be specific to a particular embodiment of the invention. do. Certain features described herein in connection with separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in connection with one embodiment may also be implemented individually or in any suitable subcombination in multiple embodiments. Moreover, the features described above act in a specific combination and may even be described above even if initially claimed to be such, but one or more features from the claimed combination can in some cases be removed from the combination, and the claimed combination Can be derived from sub-combinations or variations of sub-combinations.

Similarly, in the drawings, the operations are shown in a specific order, but it should be understood that these operations are performed in the specific order or sequential order shown, or that all illustrated operations are performed to obtain a desired result. Can not be done. Multitasking and parallel processing may be advantageous in certain situations. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and the described program components and systems are generally integrated together within a single software product. It should be understood that it may or may be packaged into multiple software products.

Certain embodiments of the invention have been described. Other embodiments are within the scope of the following claims. For example, the operations recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

Claims (15)

A method performed by a data processing device, comprising:
Receiving relevance scores for an individual set of web resources, each relevance score representing a relevance of a web resource corresponding to a search query;
For each web resource in the set of web resources, obtaining a plurality of similarity scores, each similarity score for the web resource is determined by each deep link to a native application. Indicates the similarity between each referenced content and the web resource;
For each of the deep links, based on each of the similarity scores between the content referenced by the deep link and the web resources and each of the relevance scores for the web resources, referenced by the deep link. Generating each quality score for the content to be made;
Selecting deep links that reference the content, each quality score meeting a threshold quality score; And
In response to the search query, providing the selected deep links to a user device with a plurality of web search results each referring to a corresponding web resource.
Method performed by a data processing apparatus comprising a. According to claim 1,
Before obtaining the plurality of similarity scores,
And for each web resource, generating the plurality of similarity scores for the web resource from the content and the web resource. According to claim 2,
Generating the plurality of similarity scores is based on one or more of n-gram Jaccard similarity, minimum hash, or locality-sensitive hashing for the plurality of similarity scores. Method performed by the data processing apparatus characterized in that. According to claim 1,
For each of the deep links, generating each quality score for the content referenced by the deep link,
For each web resource, calculating a product of each of the similarity scores between the content referenced by the deep link and the web resource and each of the relevance scores for the web resource; And
Summing each of the products to produce respective quality scores
Method performed by the data processing apparatus comprising a. According to claim 1,
Each deep link for each native application, when selected on the user device, causes the respective native application to instantiate an instance of each native application on which the content referenced in the deep link is displayed. A method performed by a data processing device. According to claim 1,
Each relevance score for each web resource is performed by a data processing apparatus characterized by being based on the rank of each web resource in a list of web resources ranked by a search engine. Way. According to claim 1,
Selecting deep links referring to the content having each quality score that satisfies the critical quality score,
A method performed by a data processing apparatus, characterized in that up to the maximum number of deep links referring to content having a quality score satisfying the critical quality score is selected. According to claim 1,
Providing the plurality of deep links to the user device together with the plurality of web search results,
For each deep link, normalizing each of the quality scores for the deep link with respect to respective relevance scores for web search results to generate a normalized relevance score for the deep link;
Ranking the web search results and deep links based on the relevance scores and the normalized relevance scores to generate a ranked list of web search results and deep links; And
Providing the user's device with a ranked list of web search results and deep links
Method performed by the data processing apparatus comprising a. According to claim 1,
A method performed by a data processing device, wherein each content referenced by each deep link is not a web resource. As a system,
One or more computers; And
A computer-readable medium connected to the one or more computers and having instructions stored thereon, the instructions, when executed by the one or more computers, cause the one or more computers to be assigned to respective neural network layers. To perform the operations, wherein the operations,
Receive relevance scores for an individual set of web resources, each relevance score representing a relevance of a web resource corresponding to a search query;
For each web resource in the set of web resources, a plurality of similarity scores are obtained, and each similarity score for a web resource is referenced by each deep link to a native application. Represents the similarity between each content being made and the web resource;
For each of the deep links, based on each of the similarity scores between the content referenced by the deep link and the web resources and each of the relevance scores for the web resources, referenced by the deep link. Generating respective quality scores for the content to be made;
Select deep links that reference the content, each quality score meeting a critical quality score; And
And in response to the search query, providing the selected deep links to a user device along with a plurality of web search results each referring to a corresponding web resource. The method of claim 10,
When the instructions are executed by the one or more computers, before the one or more computers obtain the plurality of similarity scores,
And for each web resource, generating the plurality of similarity scores for the web resource from the content and the web resource. The method of claim 10,
The instructions, when executed by the one or more computers, cause the one or more computers to generate, for each of the deep links, a respective quality score for the content referenced by the deep link,
For each web resource, each product of each of the similarity scores between the content referenced by the deep link and the web resource and each of the relevance scores for the web resource is calculated; And
And generating each quality score by summing the respective products to produce each quality score. The method of claim 10,
The instructions, when executed by the one or more computers, cause the one or more computers to provide the plurality of deep links to the user device along with the plurality of web search results,
For each deep link, normalize each of the quality scores for the deep link for each relevance score for web search results to generate a normalized relevance score for the deep link;
Ranking the web search results and deep links based on the relevance scores and the normalized relevance scores to generate a ranked list of web search results and deep links; And
And providing the user device with an ordered list of web search results and deep links, thereby providing the plurality of deep links to the user device. The method of claim 10,
A system characterized in that each content referenced by each deep link is not a web resource. A computer-readable medium having instructions stored thereon, which, when executed by one or more computers, cause the one or more computers to perform the method according to any one of claims 1 to 9. A computer-readable medium, characterized in that.

Images